The magnetic suspension system of a low-speed maglev train is presented in this paper. The design and realization of the magnetic suspension controller are discussed, and a nonlinear mathematical model of the magnetic suspension system is built. Then, the proportion integration differentiation controller is investigated, which indicates that it is sensitive to disturbances. To reject the disturbance and parameter perturbations, an adaptive neural-fuzzy sliding mode controller is presented, which employs a sliding mode control, adaptive-fuzzy approximator, and the neural-fuzzy switching law. The sufficient simulation and experimental results are included to demonstrate that the presented robust controller significantly reduces the impact of the disturbance and parameter perturbations with a smooth control current.

Adaptive Neural-Fuzzy Robust Position Control Scheme for Maglev Train Systems With Experimental Verification

When the electromagnetic suspension (EMS) type maglev vehicle is traveling over a track, the airgap must be maintained between the electromagnet and the track to prevent contact with that track. Because of the open-loop instability of the EMS system, the current must be actively controlled to maintain the target airgap. However, the maglev system suffers from the strong nonlinearity, force saturation, track flexibility, and feedback signals with network time-delay, hence making the controller design even more difficult. In this article, the minimum levitation unit of the maglev vehicle system has been established. An amplitude saturation controller (ASC), which can ensure the generation of only saturated unidirectional attractive force, is thus proposed. The stability and convergence of the closed-loop signals are proven based on the Lyapunov method. Subsequently, ASC is improved based on the radial basis function neural networks, and a neural network-based supervisor controller (NNBSC) is thus designed. The ASC plays the main role in the initial stage. As the neural network learns the control trend, it will gradually transition to the neural network controller. Simulation results are provided to illustrate the specific merit of the NNBSC. The hardware experimental results of a full-scale IoT EMS maglev train are included to validate the effectiveness and robustness of the presented control method as regards to time delay.

RBF Neural Network-Based Supervisor Control for Maglev Vehicles on an Elastic Track With Network Time Delay

This paper employs a 2-DOF (degree of freedom) PID controller for compensating a physical magnetic levitation system. It is shown that because of having a feedforward gain in the proposed 2-DOF PID control, the transient performance of the compensated system can be changed in a desired manner unlike the conventional 1-DOF PID control. It is also shown that for a choice of PID parameters, although the theoretical loop robustness is the same for both the compensated systems, in real-time, 2-DOF PID control may provide superior robustness if a suitable choice of the feedforward parameter is made. The results are verified through simulations and experiments.

Design and implementation of a 2-DOF PID compensation for magnetic levitation systems.

This paper addresses the active control of stationary self-excited vibration, which degrades the stability of the levitation control, decreases the ride comfort, and restricts the construction cost of the maglev system. First, a minimum interaction model containing a flexible bridge and a single levitation unit is presented. Based on the minimum interaction model, the principle underlying the self-excited vibration is explored. It shows that the active property of the levitation system is the root of self-excited vibration. Consider that the energy of vibration may be absorbed by the electromagnetic energy harvester (EEH), so that a technique applying it to the bridge is proposed, and the stability of the combined system is analyzed. However, its hardware structure is complicated, and the cost of construction is prohibitive. Then the novel conception of the virtual EEH is brought forward, which uses the electromagnetic force to emulate the force of a real energy harvester acting on the bridge. With the estimation of the vertical velocity of the bridge and the frequency of vibration, the self-oscillatory is avoided as well by adding an extra control instruction to the electromagnet. After building the overall dynamic model with details, numerical simulations and field experiments are carried out, and the results illustrating the improvement of stability are provided and analyzed.

The Active Control of Maglev Stationary Self-Excited Vibration With a Virtual Energy Harvester

Vehicle dynamics and the need to satisfy ride quality requirements have long been recognized as crucial to the commercial success of passenger carrying transportation systems. Design concepts for maglev systems are no exception. The work reported here was undertaken with the intention of summarizing information that has been accumulated worldwide and that is relevant to dynamic stability of repulsive-force maglev systems, assimilating that information, and gaining an understanding of the factors that influence that stability. Included in the paper is a discussion and comparison of results acquired from some representative tests of large-scale vehicles on linear test tracks, together with analytical and laboratory-scale investigations of stability and dynamics of EDS systems. The paper also summarizes the R and D activities at Argonne National Laboratory since 1991 to study the nature of the forces that are operative in an EDS system and the dynamic stability of such systems.

Review of dynamic stability of repulsive-force maglev suspension systems

Nonlinear Control Scheme for the Levitation Module of Maglev Train

The Electromagnet Suspension (EMS) maglev train uses controlled electromagnetic forces to achieve suspension, and self-excited vibration may occur due to the flexibility of the track. In this article, the harmonic balance method is applied to investigate the amplitude of the self-excited vibration, and it is found that the amplitude of the vibration depends on the voltage of the power supplier. Based on this observation, a vibration amplitude control method, which controls the amplitude of the vibration by adjusting the voltage of the power supplier, is proposed to attenuate the vibration. A PI controller is designed to control the amplitude of the vibration at a given level. The effectiveness of this method shows a good prospect for its application to commercial maglev systems.

Amplitude control of the track-induced self-excited vibration for a maglev system.

The EMS (Electromagnet Suspension) maglev system uses controlled electromagnetic forces to achieve suspension, and self-excited vibration may occur due to the flexibility of the track. The harmonic balance method is applied in this article to investigate the amplitude of the self-excited vibration, and it is found that the amplitude of the vibration depends on the voltage of the power supplier. Based on this conclusion, a vibration amplitude control method, which controls the amplitude of the vibration by adjusting the voltage of the power supplier, is proposed to attenuate the vibration. A P1 controller is designed to control the amplitude of the vibration at a given level. The effectiveness of this method is validated by a simulation, which shows a good prospect for its application to real maglev systems.

Amplitude control of the track-induced self-excited vibration in a maglev system

A new model about nonlinear discrete-time tracking-differentiator which can quickly track an input signal without overshoot and chattering and can produce a well differential signal is presented. Itpsilas filtering capability is studied extensively, The result shows that this tracking-differentiator has excellent capability of remove noise. Finally, a simulation and analysis of experimental data is given, as a result the feasibility of the tracking-differentiatorpsilas filtering method is confirmed.

Research on a new nonlinear discrete-time tracking-differentiator filtering characteristic

The suspension module control system model has been established based on MIMO (multiple input and multiple output) state feedback linearization. We have completed decoupling between double suspension points, and the new decoupling method has been applied to CMS04 magnetic suspension vehicle in national mid-low-speed maglev experiment field of Tangshan city in China. Double suspension system model is very accurate for investigating stability property of maglev control system. When magnetic flux signal is taken back to the suspension control system, the suspension module's antijamming capacity for resisting suspension load variety has been proved. Also, the external force interference has been enhanced. As a result, the robustness and stability properties of double-electromagnet suspension control system have been enhanced.

/pdf/decoupling-suspension-controller-based-on-magnetic-flux-37lt00pyj8.pdf

Decoupling suspension controller based on magnetic flux feedback.

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